Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms

Nature-inspired optimization-based clustering techniques are powerful, robust and more sophisticated than the conventional clustering methods due to their stochastic and heuristic characteristics. Unfortunately, these algorithms suffer with several drawbacks such as the tendency to be trapped or stagnate into local optima and slow convergence rates. The latter drawbacks are consequences of the difficulty in balancing the exploration and exploitation processes which directly affect the final quality of the clustering solutions. Hence, this research has proposed three enhanced frameworks, namely, Optimized Gravitational-based (OGC), Density-Based Particle Swarm Optimization (DPSO), and Variance-based Differential Evolution with an Optional Crossover (VDEO) frameworks for data clustering. In the OGC framework, the exhibited explorative search behavior of the Gravitational Clustering (GC) algorithm has been addressed by (i) eliminating the agent velocity accumulation, and (ii) integrating an initialization method of agents using variance and median to subrogate the exploration process. Moreover, the balance between the exploration and exploitation processes in the DPSO framework is considered using a combination of (i) a kernel density estimation technique associated with new bandwidth estimation method and (ii) estimated multi-dimensional gravitational learning coefficients. Lastly, (i) a single-based solution representation, (ii) a switchable mutation scheme, (iii) a vector-based estimation of the mutation factor, and (iv) an optional crossover strategy are proposed in the VDEO framework. The overall performances of the three proposed frameworks have been compared with several current state-of-the-art clustering algorithms on 15 benchmark datasets from the UCI repository. The experimental results are also thoroughly evaluated and verified via non-parametric statistical analysis. Based on the obtained experimental results, the OGC, DPSO, and VDEO frameworks achieved an average enhancement up to 24.36%, 9.38%, and 11.98% of classification accuracy, respectively. All the frameworks also achieved the first rank by the Friedman aligned-ranks (FA) test in all evaluation metrics. Moreover, the three frameworks provided convergent performances in terms of the repeatability. Meanwhile, the OGC framework obtained a significant performance in terms of the classification accuracy, where the VDEO framework presented a significant performance in terms of cluster compactness. On the other hand, the DPSO framework favored the balanced state by producing very competitive results compared to the OGC and DPSO in both evaluation metrics. As a conclusion, balancing the search behavior notably enhanced the overall performance of the three proposed frameworks and made each of them an excellent tool for data clustering

Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naïve Bayes Classifiers

Classification problems are increasing in various important applications such as text categorization, images, medical imaging diagnosis and bimolecular analysis etc. due to large amount of attribute set. Feature extraction methods in case of large dataset play an important role to reduce the irrelevant feature and thereby increases the performance of classifier algorithm. There exist various methods based on machine learning for text and image classification. These approaches are utilized for dimensionality reduction which aims to filter less informative and outlier data. Therefore, these approaches provide compact representation and computationally better tractable accuracy. At the same time, these methods can be challenging if the search space is doubled multiple time. To optimize such challenges, a hybrid approach is suggested in this paper. The proposed approach uses differential evolution (DE) for feature selection with naïve bayes (NB) and support vector machine (SVM) classifiers to enhance the performance of selected classifier. The results are verified using text and image data which reflects improved accuracy compared with other conventional techniques. A 25 benchmark datasets (UCI) from different domains are considered to test the proposed algorithms.  A comparative study between proposed hybrid classification algorithms are presented in this work. Finally, the experimental result shows that the differential evolution with NB classifier outperforms and produces better estimation of probability terms. The proposed technique in terms of computational time is also feasible

DEoptim: An R Package for Global Optimization by Differential Evolution

This article describes the R package DEoptim, which implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector. The implementation of differential evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated by case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-switching generalized autoregressive conditional heteroskedasticity model for the returns of the Swiss Market Index.

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based meta-heuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis was also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm

Application of Differential Evolution algorithms to multi-objective optimization problems in mixed-oxide fuel assembly design

Multi-objective optimization of nuclear engineering fuel assembly design problems is particularly difficult due to the highly non-linear interactions of a large number of possible variables. In addition, effective optimization algorithms are often highly problem-dependent and require extensive tuning, which reduces their applicability to the real world. To address this issue, Differential Evolution (DE) algorithms have been proposed as a new and effective method for heterogeneous fuel assembly optimization design problems. This paper presents the first complete study to investigate their applicability and performance. Firstly, two multi- objective DE algorithms have their performance compared against an Evolutionary Algorithm (EA) from the literature in optimizing a CORAIL mixed-oxide (MOX) fuel assembly for maximum plutonium content and minimum power peaking factor. Statistical analysis of the results shows the DE algorithms exhibit superior performance to the EA. The DE algorithms are then used to optimize a MOX fuel assembly with gadolinia poison, with results showing DE produces assembly designs comparable in performance to those in the literature. Finally, a sensitivity study is conducted on the control parameters of the better performing of the DE algorithms. Results indicate DE performance remains consistent for a wide range of values of both control parameters, suggesting the algorithm is able to perform effectively without requiring user expertise or effort to find the ‘optimal’ control parameter settings

Development Of A Methodology For Fast Optimization Of Building Retrofit And Decision Making Support

The condition of current building stock in the United States raises the question of whether the energy performance of existing buildings can ever be environmentally sustainable. In the United States, buildings accounted for 39% of total energy consumption and 72% of total electricity consumption (USEPA 2009). In addition, current building energy use is projected to increase by 1.7% annually until 2025 (J.D. Ryan 2004). The great potential for energy reduction in existing buildings has created opportunities in building energy retrofit projects (Noris et al. 2013). A building renovation project must not only be affordable, taking into account factors such as investor budgets, payback period, economic risks and uncertainties, but also create a thermally comfortable indoor environment and is sustainable through its lifetime. The research objective of this dissertation is to develop a novel method to optimize the performance of buildings during their post-retrofit period in the future climate. The dissertation is organized in three sections: a) Develop a data-driven method for the hourly projection of energy use in the coming years, taking into account global climate change (GCC). Using machine learning algorithms, a validated data-driven model is used to predict the building’s future hourly energy use based on simulation results generated by future extreme year weather data and it is demonstrated that GCC will change the optimal solution of future energy conservation measure (ECM) combination. b) Develop a simplified building performance simulation tool based on a dynamic hourly simulation algorithm taking into account the thermal flux among zones. The tool named SimBldPy is tested on EnergyPlus models with DOE reference buildings. Its performance and fidelity in simulating hourly energy use with different heating and cooling set points in each zone, under various climate conditions, and with multiple ECMs being applied to the building, has been validated. This tool and modeling method could be used for rapid modeling and assessment of building energy for a variety of ECM options. c) Use a non-dominated sorting technique to complete the multi-objective optimization task and design a schema to visualize optimization results and support the decision-making process after obtaining the multi-objective optimization results. By introducing the simplified hourly simulation model and the random forest (RF) models as a substitute for traditional energy simulation tools in objective function assessment, certain deep retrofit problem can be quickly optimized. Generated non-dominated solutions are rendered and displayed by a layered schema using agglomerative hierarchical clustering technique. The optimization method is then implemented on a Penn campus building for case study, and twenty out of a thousand retrofit plans can be recommended using the proposed decision-making method. The proposed decision making support framework is demonstrated by its robustness to the problem of deep retrofit optimization and is able to provide support for brainstorming and enumerate various possibilities during the process of making the decision